A Code-Description Representation Learning Model Based on Attention

Abstract

Code search is to retrieve source code given a query. By deep learning, the existing work embeds source code and its description into a shared vector space; however, this space is so general that each code token is associated with each description term. In this paper, we propose a code-description representation learning model (CDRL) based on attention. This model refines the general shared space into the specific one. In such space, only semantically related code tokens and description terms are associated. The experimental results show that this model could retrieve relevant source code effectively and outperform the state-of-the-art method (e.g., CODEnn and QECK) by 4-8% in terms of precision when the first query result is inspected.